TY - JOUR
T1 - Observer and Data-Driven-Model-Based Fault Detection in Power Plant Coal Mills
AU - Odgaard, Peter Fogh
AU - Lin, B.
AU - Jørgensen, S. B.
PY - 2008
Y1 - 2008
N2 - This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squaresmethods. The residual between processmeasurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-drivenmodel is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe.All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.
AB - This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squaresmethods. The residual between processmeasurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-drivenmodel is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe.All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.
KW - Fault Detection
KW - Data-driven Detection
KW - Power plants
KW - Unknown input observer
U2 - 10.1109/TEC.2007.914185
DO - 10.1109/TEC.2007.914185
M3 - Journal article
SN - 0885-8969
VL - 23
SP - 659
EP - 668
JO - IEEE Transactions on Energy Conversion
JF - IEEE Transactions on Energy Conversion
IS - 2
ER -